DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery

arXiv:2311.13716v2 Announce Type: replace
Abstract: Semi-supervised learning aims to help reduce the cost of the manual labelling process by leveraging valuable features extracted from a substantial pool of unlabeled data alongside a limited set of labelled data during the training phase. Since pixel-level manual labelling in large-scale remote sensing imagery is expensive, semi-supervised learning becomes an appropriate solution to this. However, most of the existing consistency learning frameworks based on network perturbation are very bulky. There is still a lack …

abstract arxiv cost cs.cv data decision features labelling networks pixel pool process reduce scale segmentation semantic semi-supervised semi-supervised learning sensing set supervised learning training type